1. Field
The following description relates to a method of correcting an output value of a geomagnetic sensor. The following description also relates to a corresponding apparatus for correcting an output value of a geomagnetic sensor. The following description further relates to a method for improving accuracy of correcting the output value of a geomagnetic sensor by tracking an estimated error, including an offset and a radius value when measuring the output value of the geomagnetic sensor every time the output value is measured.
2. Description of Related Art
Data measured using a geomagnetic sensor includes a bias aside from a geomagnetic field. For example, the bias may be the result of a steel effect of a physical feature that causes a fixed diffusion or offset of magnetism, a measurement error of a sensor, an orthogonal error of respective axis, and a soft-iron effect around the sensor. A data correction of a geomagnetic sensor may separate the actual element of a global magnetic field and correct for bias by removing expected bias from the data that is measured by a geomagnetic sensor. In addition, correcting for performance means that many biases may be reduced.
The data that is measured from a 2-axis or 3-axis geomagnetic sensor has a feature of being distributed on a circular or spherical surface. Furthermore, estimations of a central point of the circle or sphere and a corresponding radius may vary in accordance with amount of the bias. Assuming that the bias is an offset with respect to a data perspective, a center point of a circle or a sphere is positioned separated by as much as the offset from the origin point. For example, a method for estimating an offset of the measured geomagnetic data generally uses the least squares method.
By estimating offset and radius using the least squares method, estimation may be possible with over three measured data points in a case of a 2-axis example and over four measured data points in a case of a 3-axis example. If only a hard-iron effect exists without estimation error in an ideal example, perfect performance may be achieved. However, in general a measured data is accompanied by various measurement noise, and accordingly a possibility of the occurrence of estimation error may not be excluded. If an amount of estimation data is large when correcting a geomagnetic sensor data using the least squares method, the portion of measurement noise is relatively small, and thus, the related art does not consider the measurement noise in this example. However, when a system using an actual geomagnetic sensor increases an amount of measurement data, it may cause process delays due to a heavier computation load, and there may accordingly be a burden of storage space thereby, so the measurement data should be processed in a predetermined amount. However, when estimating an offset based on a measured data group of predetermined amount, distribution of estimated values between groups is generated and accuracy decreases when an estimation error is large.